#' @title Boosted Generalized Additive Survival Learner
#' @author RaphaelS1
#' @name mlr_learners_surv.mboost
#'
#' @description
#' Model-based boosting for survival analysis.
#' Calls [mboost::mboost()] from \CRANpkg{mboost}.
#'
#' @template learner
#' @templateVar id surv.mboost
#'
#' @details
#' `distr` prediction made by [mboost::survFit()].
#'
#' @references
#' `r format_bib("buhlmann2003boosting")`
#'
#' @export
#' @template seealso_learner
#' @template example
delayedAssign(
  "LearnerSurvMBoost",
  R6Class("LearnerSurvMBoost",
    inherit = mlr3proba::LearnerSurv,
    public = list(
      #' @description
      #' Creates a new instance of this [R6][R6::R6Class] class.
      initialize = function() {
        ps = ps(
          family = p_fct(default = "coxph",
            levels = c(
              "coxph", "weibull", "loglog", "lognormal", "gehan", "cindex",
              "custom"), tags = c("train", "predict")),
          custom.family = p_uty(tags = "train"),
          nuirange = p_uty(default = c(0, 100), tags = "train"),
          offset = p_dbl(tags = "train"),
          center = p_lgl(default = TRUE, tags = "train"),
          mstop = p_int(default = 100L, lower = 0L, tags = "train"),
          nu = p_dbl(default = 0.1, lower = 0, upper = 1, tags = "train"),
          risk = p_fct(default = "inbag",
            levels = c("inbag", "oobag", "none"), tags = "train"),
          stopintern = p_lgl(default = FALSE, tags = "train"),
          trace = p_lgl(default = FALSE, tags = "train"),
          oobweights = p_uty(default = NULL, tags = "train"),
          baselearner = p_fct(default = "bbs",
            levels = c("bbs", "bols", "btree"), tags = "train"),
          sigma = p_dbl(default = 0.1, lower = 0, upper = 1,
            tags = "train"),
          ipcw = p_uty(default = 1, tags = "train"),
          na.action = p_uty(default = stats::na.omit, tags = "train")
        )

        ps$values = list(family = "coxph")
        ps$add_dep("sigma", "family", CondEqual$new("cindex"))
        ps$add_dep("ipcw", "family", CondEqual$new("cindex"))

        super$initialize(
          id = "surv.mboost",
          param_set = ps,
          feature_types = c("integer", "numeric", "factor", "logical"),
          predict_types = c("distr", "crank", "lp"),
          properties = c("weights", "importance", "selected_features"),
          packages = c("mlr3extralearners", "mboost"),
          man = "mlr3extralearners::mlr_learners_surv.mboost",
          label = "Boosted Generalized Additive Model"
        )
      },

      #' @description
      #' The importance scores are extracted with the function [mboost::varimp()] with the
      #' default arguments.
      #' @return Named `numeric()`.
      importance = function() {
        if (is.null(self$model)) {
          stopf("No model stored")
        }

        vimp = as.numeric(mboost::varimp(self$model))
        names(vimp) = unname(stats::variable.names(self$model))

        sort(vimp, decreasing = TRUE)
      },

      #' @description
      #' Selected features are extracted with the function [mboost::variable.names.mboost()], with
      #' `used.only = TRUE`.
      #' @return `character()`.
      selected_features = function() {
        if (is.null(self$model)) {
          stopf("No model stored")
        }

        unname(stats::variable.names(self$model, usedonly = TRUE))
      }
    ),

    private = list(
      .train = function(task) {

        # parameter custom.family takes precedence over family
        if (!is.null(self$param_set$values$custom.family)) {
          self$param_set$values$family = "custom"
        }

        pars = self$param_set$get_values(tags = "train")

        if ("weights" %in% task$properties) {
          pars$weights = task$weights$weight
        }

        # Save control settings and return on exit
        saved_ctrl = mboost::boost_control()
        on.exit(invoke(mboost::boost_control, .args = saved_ctrl))
        is_ctrl_pars = (names(pars) %in% names(saved_ctrl))

        # ensure only relevant pars passed to fitted model
        if (any(is_ctrl_pars)) {
          pars$control = do.call(mboost::boost_control, pars[is_ctrl_pars])
          pars = pars[!is_ctrl_pars]
        }

        family = switch(pars$family,
          coxph = mboost::CoxPH(),
          weibull = invoke(mboost::Weibull,
            .args = pars[names(pars) %in% formalArgs(mboost::Weibull)]),
          loglog = invoke(mboost::Loglog,
            .args = pars[names(pars) %in% formalArgs(mboost::Loglog)]),
          lognormal = invoke(mboost::Lognormal,
            .args = pars[names(pars) %in% formalArgs(mboost::Lognormal)]),
          gehan = mboost::Gehan(),
          cindex = invoke(mboost::Cindex,
            .args = pars[names(pars) %in% formalArgs(mboost::Cindex)]),
          custom = pars$custom.family
        )

        # FIXME - until issue closes
        pars = pars[!(names(pars) %in% formalArgs(mboost::Weibull))]
        pars = pars[!(names(pars) %in% formalArgs(mboost::Cindex))]
        pars = pars[!(names(pars) %in% c("family", "custom.family"))]

        with_package("mboost", {
          invoke(mboost::mboost,
            formula = task$formula(task$feature_names),
            data = task$data(), family = family, .args = pars)
        })
      },

      .predict = function(task) {

        newdata = ordered_features(task, self)
        # predict linear predictor
        lp = as.numeric(invoke(predict, self$model, newdata = newdata, type = "link"))

        # predict survival
        if (is.null(self$param_set$values$family) || self$param_set$values$family == "coxph") {
          survfit = invoke(mboost::survFit, self$model, newdata = newdata)

          mlr3proba::.surv_return(times = survfit$time,
            surv = t(survfit$surv),
            lp = lp)
        } else {
          mlr3proba::.surv_return(lp = -lp)
        }


        # FIXME - RE-ADD ONCE INTERPRETATION IS CLEAR
        # response = NULL
        # if (!is.null(self$param_set$values$family)) {
        #   if (self$param_set$values$family %in% c("weibull", "loglog", "lognormal", "gehan")) {
        #     response = exp(lp)
        #   }
        # }
      }
    )
  )
)

.extralrns_dict$add("surv.mboost", function() LearnerSurvMBoost$new())
